The main idea of dilated convolution is to insert zeros between pixels of convolution kernels. It is widely used in segmentation [1]}, [2]} to enlarge the receptive fields while keeping the resolutions of feature maps. As the size of its receptive field can be easily changed by adjusting its dilation rate, dilated convolution is also used to aggregate multi-scale context information. For example, in [3]}, the outputs of convolutional layers with different dilation rates are fused to exploit multi-scale context information. And in [4]}, a similar idea is adopted in an atrous spatial pyramid pooling (ASPP) module. However, these dilation rates of different layers are manually set and can only be integers, which are not flexible enough. Instead, the dilation rates in ADC can be fractional and are adaptively generated, which enables it to learn more suitable receptive fields for objects of various sizes. Besides, every dilation group in ADC can represent features at a scale, which enables ADC to fuse richer multi-scale information yet in a simpler way.
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